{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f8d0cf4c",
   "metadata": {},
   "source": [
    "## 链家租房信息的分析与挖掘"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6afd0ea",
   "metadata": {},
   "source": [
    "1. 我们身为即将毕业的大学生面临着进入社会进行工作，第一件事就是要解决住所问题\n",
    "2. 因为合租和整租是两种不同类型,这里我们是以整租、较热门房屋面积类型为样本来进行挖掘\n",
    "2. 租房的价格高低和哪些特征具有紧密联系\n",
    "3. 挖掘出哪些特征对房租的影响最大\n",
    "4. 构造一个预测模型，只要输入对应的特征数据，预测对应房租价格"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd7fe59",
   "metadata": {},
   "source": [
    "### 一、导入数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b4cfc2",
   "metadata": {},
   "source": [
    "本数据来源链家，通过爬虫来获取到对应的特征数据，保存于mysql，由于该爬虫我部署于Linux上，所以写了一个python脚本将本数据导为home.csv用来进行分析与挖掘(https://sz.lianjia.com/zufang/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512fbf9e",
   "metadata": {},
   "source": [
    "特征说明：\n",
    "\n",
    "* type：招租的类型，有整租和合租\n",
    "* home_name：房名\n",
    "* home_type：房型\n",
    "* home_dire：房的朝向\n",
    "* admi：房所处的行政区\n",
    "* sub_station：靠近的地铁站\n",
    "* adress：详细地址\n",
    "* area：房面积\n",
    "* price：房租（月）\n",
    "* floor：楼层\n",
    "* broker：房屋经纪人的名\n",
    "* broker_no：房屋经纪人的编号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b32b12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a50a427",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入数据，并写入特征列\n",
    "data = pd.read_csv(\"home.csv\", header=None)\n",
    "data.columns=[\"type\", \"home_name\", \"home_type\", \"home_dire\", \"admi\", \"sub_station\", \"adress\", \"area\", \"price\", \"floor\", \"broker\", \"broker_no\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1912daa7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>home_name</th>\n",
       "      <th>home_type</th>\n",
       "      <th>home_dire</th>\n",
       "      <th>admi</th>\n",
       "      <th>sub_station</th>\n",
       "      <th>adress</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "      <th>floor</th>\n",
       "      <th>broker</th>\n",
       "      <th>broker_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>整租</td>\n",
       "      <td>海云轩</td>\n",
       "      <td>2室2厅1卫</td>\n",
       "      <td>西南</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>翻身</td>\n",
       "      <td>海云轩</td>\n",
       "      <td>73.00㎡</td>\n",
       "      <td>7800</td>\n",
       "      <td>低楼层</td>\n",
       "      <td>刘世林</td>\n",
       "      <td>J303120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>整租</td>\n",
       "      <td>招商前海领尚公馆</td>\n",
       "      <td>1室0厅1卫</td>\n",
       "      <td>东南</td>\n",
       "      <td>南山区</td>\n",
       "      <td>前海</td>\n",
       "      <td>招商前海·领尚公馆</td>\n",
       "      <td>43.00㎡</td>\n",
       "      <td>6000</td>\n",
       "      <td>低楼层</td>\n",
       "      <td>彭超</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>整租</td>\n",
       "      <td>万科时代广场</td>\n",
       "      <td>2室2厅1卫</td>\n",
       "      <td>东南</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>龙岗中心城</td>\n",
       "      <td>万科时代广场</td>\n",
       "      <td>66.51㎡</td>\n",
       "      <td>4000</td>\n",
       "      <td>低楼层</td>\n",
       "      <td>郭世雄</td>\n",
       "      <td>J182921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>整租</td>\n",
       "      <td>中粮天悦壹号</td>\n",
       "      <td>3室2厅2卫</td>\n",
       "      <td>东北</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>新安</td>\n",
       "      <td>中粮天悦壹号</td>\n",
       "      <td>89.00㎡</td>\n",
       "      <td>9500</td>\n",
       "      <td>高楼层</td>\n",
       "      <td>李君</td>\n",
       "      <td>J093856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>整租</td>\n",
       "      <td>依山郡花园三期</td>\n",
       "      <td>1室1厅1卫</td>\n",
       "      <td>南</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>龙岗中心城</td>\n",
       "      <td>依山郡花园三期</td>\n",
       "      <td>47.00㎡</td>\n",
       "      <td>2800</td>\n",
       "      <td>中楼层</td>\n",
       "      <td>赵云峰</td>\n",
       "      <td>J289682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  type home_name home_type home_dire admi sub_station     adress    area  \\\n",
       "0   整租       海云轩    2室2厅1卫        西南  宝安区          翻身        海云轩  73.00㎡   \n",
       "1   整租  招商前海领尚公馆    1室0厅1卫        东南  南山区          前海  招商前海·领尚公馆  43.00㎡   \n",
       "2   整租    万科时代广场    2室2厅1卫        东南  龙岗区       龙岗中心城     万科时代广场  66.51㎡   \n",
       "3   整租    中粮天悦壹号    3室2厅2卫        东北  宝安区          新安     中粮天悦壹号  89.00㎡   \n",
       "4   整租   依山郡花园三期    1室1厅1卫         南  龙岗区       龙岗中心城    依山郡花园三期  47.00㎡   \n",
       "\n",
       "   price floor broker broker_no  \n",
       "0   7800   低楼层    刘世林   J303120  \n",
       "1   6000   低楼层     彭超      None  \n",
       "2   4000   低楼层    郭世雄   J182921  \n",
       "3   9500   高楼层     李君   J093856  \n",
       "4   2800   中楼层    赵云峰   J289682  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据导入成功\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb83340",
   "metadata": {},
   "source": [
    "### 二、数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3e4706db",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36415 entries, 0 to 36414\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   type         36415 non-null  object\n",
      " 1   home_name    36415 non-null  object\n",
      " 2   home_type    36415 non-null  object\n",
      " 3   home_dire    36415 non-null  object\n",
      " 4   admi         36415 non-null  object\n",
      " 5   sub_station  36415 non-null  object\n",
      " 6   adress       36415 non-null  object\n",
      " 7   area         36415 non-null  object\n",
      " 8   price        36415 non-null  int64 \n",
      " 9   floor        36415 non-null  object\n",
      " 10  broker       36415 non-null  object\n",
      " 11  broker_no    36415 non-null  object\n",
      "dtypes: int64(1), object(11)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "975b229c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>home_name</th>\n",
       "      <th>home_type</th>\n",
       "      <th>home_dire</th>\n",
       "      <th>admi</th>\n",
       "      <th>sub_station</th>\n",
       "      <th>adress</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "      <th>floor</th>\n",
       "      <th>broker</th>\n",
       "      <th>broker_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>整租</td>\n",
       "      <td>海云轩</td>\n",
       "      <td>2室2厅1卫</td>\n",
       "      <td>西南</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>翻身</td>\n",
       "      <td>海云轩</td>\n",
       "      <td>73.00㎡</td>\n",
       "      <td>7800</td>\n",
       "      <td>低楼层</td>\n",
       "      <td>刘世林</td>\n",
       "      <td>J303120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>整租</td>\n",
       "      <td>招商前海领尚公馆</td>\n",
       "      <td>1室0厅1卫</td>\n",
       "      <td>东南</td>\n",
       "      <td>南山区</td>\n",
       "      <td>前海</td>\n",
       "      <td>招商前海·领尚公馆</td>\n",
       "      <td>43.00㎡</td>\n",
       "      <td>6000</td>\n",
       "      <td>低楼层</td>\n",
       "      <td>彭超</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>整租</td>\n",
       "      <td>万科时代广场</td>\n",
       "      <td>2室2厅1卫</td>\n",
       "      <td>东南</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>龙岗中心城</td>\n",
       "      <td>万科时代广场</td>\n",
       "      <td>66.51㎡</td>\n",
       "      <td>4000</td>\n",
       "      <td>低楼层</td>\n",
       "      <td>郭世雄</td>\n",
       "      <td>J182921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>整租</td>\n",
       "      <td>中粮天悦壹号</td>\n",
       "      <td>3室2厅2卫</td>\n",
       "      <td>东北</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>新安</td>\n",
       "      <td>中粮天悦壹号</td>\n",
       "      <td>89.00㎡</td>\n",
       "      <td>9500</td>\n",
       "      <td>高楼层</td>\n",
       "      <td>李君</td>\n",
       "      <td>J093856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>整租</td>\n",
       "      <td>依山郡花园三期</td>\n",
       "      <td>1室1厅1卫</td>\n",
       "      <td>南</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>龙岗中心城</td>\n",
       "      <td>依山郡花园三期</td>\n",
       "      <td>47.00㎡</td>\n",
       "      <td>2800</td>\n",
       "      <td>中楼层</td>\n",
       "      <td>赵云峰</td>\n",
       "      <td>J289682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>整租</td>\n",
       "      <td>圣淘沙骏园</td>\n",
       "      <td>3室2厅1卫</td>\n",
       "      <td>东南</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>西乡</td>\n",
       "      <td>圣淘沙骏园</td>\n",
       "      <td>76.00㎡</td>\n",
       "      <td>7200</td>\n",
       "      <td>中楼层</td>\n",
       "      <td>覃武洲</td>\n",
       "      <td>J029902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>合租</td>\n",
       "      <td>聚宝花园</td>\n",
       "      <td>5室1厅3卫</td>\n",
       "      <td>南卧</td>\n",
       "      <td>罗湖区</td>\n",
       "      <td>莲塘</td>\n",
       "      <td>聚宝花园</td>\n",
       "      <td>25.00㎡</td>\n",
       "      <td>2100</td>\n",
       "      <td>高楼层</td>\n",
       "      <td>韩蔚</td>\n",
       "      <td>J038061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>合租</td>\n",
       "      <td>石鸿花园</td>\n",
       "      <td>4室2厅2卫</td>\n",
       "      <td>东南卧</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>翻身</td>\n",
       "      <td>石鸿花园</td>\n",
       "      <td>20.00㎡</td>\n",
       "      <td>1600</td>\n",
       "      <td>中楼层</td>\n",
       "      <td>赵雪琴</td>\n",
       "      <td>J254844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>整租</td>\n",
       "      <td>帝景峰</td>\n",
       "      <td>3室2厅1卫</td>\n",
       "      <td>东</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>布吉大芬</td>\n",
       "      <td>帝景峰</td>\n",
       "      <td>73.00㎡</td>\n",
       "      <td>3600</td>\n",
       "      <td>低楼层</td>\n",
       "      <td>胡志伟</td>\n",
       "      <td>J250579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>合租</td>\n",
       "      <td>深港新村</td>\n",
       "      <td>3室1厅1卫</td>\n",
       "      <td>南卧</td>\n",
       "      <td>罗湖区</td>\n",
       "      <td>新秀</td>\n",
       "      <td>深港新村</td>\n",
       "      <td>10.60㎡</td>\n",
       "      <td>1990</td>\n",
       "      <td>中楼层</td>\n",
       "      <td>自如社区顾问</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  type home_name home_type home_dire admi sub_station     adress    area  \\\n",
       "0   整租       海云轩    2室2厅1卫        西南  宝安区          翻身        海云轩  73.00㎡   \n",
       "1   整租  招商前海领尚公馆    1室0厅1卫        东南  南山区          前海  招商前海·领尚公馆  43.00㎡   \n",
       "2   整租    万科时代广场    2室2厅1卫        东南  龙岗区       龙岗中心城     万科时代广场  66.51㎡   \n",
       "3   整租    中粮天悦壹号    3室2厅2卫        东北  宝安区          新安     中粮天悦壹号  89.00㎡   \n",
       "4   整租   依山郡花园三期    1室1厅1卫         南  龙岗区       龙岗中心城    依山郡花园三期  47.00㎡   \n",
       "5   整租     圣淘沙骏园    3室2厅1卫        东南  宝安区          西乡      圣淘沙骏园  76.00㎡   \n",
       "6   合租      聚宝花园    5室1厅3卫        南卧  罗湖区          莲塘       聚宝花园  25.00㎡   \n",
       "7   合租      石鸿花园    4室2厅2卫       东南卧  宝安区          翻身       石鸿花园  20.00㎡   \n",
       "8   整租       帝景峰    3室2厅1卫         东  龙岗区        布吉大芬        帝景峰  73.00㎡   \n",
       "9   合租      深港新村    3室1厅1卫        南卧  罗湖区          新秀       深港新村  10.60㎡   \n",
       "\n",
       "   price floor  broker broker_no  \n",
       "0   7800   低楼层     刘世林   J303120  \n",
       "1   6000   低楼层      彭超      None  \n",
       "2   4000   低楼层     郭世雄   J182921  \n",
       "3   9500   高楼层      李君   J093856  \n",
       "4   2800   中楼层     赵云峰   J289682  \n",
       "5   7200   中楼层     覃武洲   J029902  \n",
       "6   2100   高楼层      韩蔚   J038061  \n",
       "7   1600   中楼层     赵雪琴   J254844  \n",
       "8   3600   低楼层     胡志伟   J250579  \n",
       "9   1990   中楼层  自如社区顾问      None  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c329839d",
   "metadata": {},
   "source": [
    "通过上表可以看出，\n",
    "\n",
    "1. 可以看出数据较为完整，没有出现空字符串的现象；\n",
    "1. 部分字段是有用，部分字段是无用的字段，比如：房名、房间地址、经纪人姓名、经纪人编号，对这些无用进行删除处理，sub_station是靠近地铁站的字段，但是在数据中却出现非地铁站的数据，故也进行删除,因为我们只分析整租类型,所以只提取整租部分的数据；\n",
    "2. 房间面积（area）是object类型，但是我们需要对其进行分析，所以将器=其转为float类型；"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac5ef0f7",
   "metadata": {},
   "source": [
    "#### 2.1 删除房名、经纪人姓名、经纪人编号、靠近的地铁站，只取整租"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56330b82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>home_type</th>\n",
       "      <th>home_dire</th>\n",
       "      <th>admi</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "      <th>floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>整租</td>\n",
       "      <td>2室2厅1卫</td>\n",
       "      <td>西南</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>73.00㎡</td>\n",
       "      <td>7800</td>\n",
       "      <td>低楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>整租</td>\n",
       "      <td>1室0厅1卫</td>\n",
       "      <td>东南</td>\n",
       "      <td>南山区</td>\n",
       "      <td>43.00㎡</td>\n",
       "      <td>6000</td>\n",
       "      <td>低楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>整租</td>\n",
       "      <td>2室2厅1卫</td>\n",
       "      <td>东南</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>66.51㎡</td>\n",
       "      <td>4000</td>\n",
       "      <td>低楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>整租</td>\n",
       "      <td>3室2厅2卫</td>\n",
       "      <td>东北</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>89.00㎡</td>\n",
       "      <td>9500</td>\n",
       "      <td>高楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>整租</td>\n",
       "      <td>1室1厅1卫</td>\n",
       "      <td>南</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>47.00㎡</td>\n",
       "      <td>2800</td>\n",
       "      <td>中楼层</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  type home_type home_dire admi    area  price floor\n",
       "0   整租    2室2厅1卫        西南  宝安区  73.00㎡   7800   低楼层\n",
       "1   整租    1室0厅1卫        东南  南山区  43.00㎡   6000   低楼层\n",
       "2   整租    2室2厅1卫        东南  龙岗区  66.51㎡   4000   低楼层\n",
       "3   整租    3室2厅2卫        东北  宝安区  89.00㎡   9500   高楼层\n",
       "4   整租    1室1厅1卫         南  龙岗区  47.00㎡   2800   中楼层"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = data.drop(['home_name', 'adress', 'broker', 'broker_no', 'sub_station'], axis = 1, inplace = False)[data[\"type\"] == \"整租\"]\n",
    "new_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4369450",
   "metadata": {},
   "source": [
    "#### 2.2 将房间字段数据处理为float类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "94467b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data[\"area\"] = new_data[\"area\"].str[:-1].astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be3a2a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>home_type</th>\n",
       "      <th>home_dire</th>\n",
       "      <th>admi</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "      <th>floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>整租</td>\n",
       "      <td>2室2厅1卫</td>\n",
       "      <td>西南</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>73.00</td>\n",
       "      <td>7800</td>\n",
       "      <td>低楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>整租</td>\n",
       "      <td>1室0厅1卫</td>\n",
       "      <td>东南</td>\n",
       "      <td>南山区</td>\n",
       "      <td>43.00</td>\n",
       "      <td>6000</td>\n",
       "      <td>低楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>整租</td>\n",
       "      <td>2室2厅1卫</td>\n",
       "      <td>东南</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>66.51</td>\n",
       "      <td>4000</td>\n",
       "      <td>低楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>整租</td>\n",
       "      <td>3室2厅2卫</td>\n",
       "      <td>东北</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>89.00</td>\n",
       "      <td>9500</td>\n",
       "      <td>高楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>整租</td>\n",
       "      <td>1室1厅1卫</td>\n",
       "      <td>南</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>47.00</td>\n",
       "      <td>2800</td>\n",
       "      <td>中楼层</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  type home_type home_dire admi   area  price floor\n",
       "0   整租    2室2厅1卫        西南  宝安区  73.00   7800   低楼层\n",
       "1   整租    1室0厅1卫        东南  南山区  43.00   6000   低楼层\n",
       "2   整租    2室2厅1卫        东南  龙岗区  66.51   4000   低楼层\n",
       "3   整租    3室2厅2卫        东北  宝安区  89.00   9500   高楼层\n",
       "4   整租    1室1厅1卫         南  龙岗区  47.00   2800   中楼层"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2c1cae",
   "metadata": {},
   "source": [
    "#### 2.3 房朝向内部的数据混乱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49b2a85d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "home_dire\n",
       "东                 746\n",
       "东/东北                1\n",
       "东/东南               14\n",
       "东/东南/南              6\n",
       "东/东南/南/西南           1\n",
       "东/东南/南/西南/西         3\n",
       "东/东南/南/西南/西北        1\n",
       "东/北                 2\n",
       "东/南                55\n",
       "东/南/西北/北            1\n",
       "东/西                 5\n",
       "东/西南              127\n",
       "东北               1487\n",
       "东南               6480\n",
       "东南/南              643\n",
       "东南/南/西南             4\n",
       "东南/西                1\n",
       "东南/西北             311\n",
       "东南/西南              86\n",
       "北                1797\n",
       "北/东北               24\n",
       "南               11835\n",
       "南/东北                6\n",
       "南/北              1201\n",
       "南/西                87\n",
       "南/西南               60\n",
       "西                1014\n",
       "西/北                 1\n",
       "西/西北                5\n",
       "西北               1212\n",
       "西北/东北               2\n",
       "西北/北                2\n",
       "西北/北/东北             2\n",
       "西南               1668\n",
       "西南/东北               1\n",
       "西南/北                1\n",
       "西南/西               22\n",
       "西南/西北             121\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.groupby(by=\"home_dire\")[[\"price\"]].count()[\"price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "640be29e",
   "metadata": {},
   "source": [
    "处理方式，只取最前一个，先按\"/\"切割，再按\"卧\"切割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6cdc9b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data[\"home_dire\"] = new_data[\"home_dire\"].str.split(\"/\",expand=True)[0]\n",
    "new_data[\"home_dire\"] = new_data[\"home_dire\"].str.split(\"卧\",expand=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a5104e4d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "home_dire\n",
       "东       962\n",
       "东北     1487\n",
       "东南     7525\n",
       "北      1821\n",
       "南     13189\n",
       "西      1020\n",
       "西北     1218\n",
       "西南     1813\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.groupby(by=\"home_dire\")[[\"price\"]].count()[\"price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d7c07fa",
   "metadata": {},
   "source": [
    "即可将房的朝向清洗统一，结果如上"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f0f43e",
   "metadata": {},
   "source": [
    "#### 2.4 楼层出现了\"未知\"\n",
    "\n",
    "未知的楼层，只有一个，所以选择删除该行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8fa250b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "floor\n",
       "中楼层    11378\n",
       "低楼层     7557\n",
       "未知         1\n",
       "高楼层    10099\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.groupby(by=\"floor\")[[\"price\"]].count()[\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4cb9820b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "floor\n",
       "中楼层    11378\n",
       "低楼层     7557\n",
       "高楼层    10099\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.drop(new_data[new_data[\"floor\"] == \"未知\"].index[0], axis=0,inplace=True)\n",
    "new_data.groupby(by=\"floor\")[[\"price\"]].count()[\"price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35f4891",
   "metadata": {},
   "source": [
    "如此便将楼层为\"未知\"，这一行剔除"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "297d36ab",
   "metadata": {},
   "source": [
    "#### 2.5 剔除房租、房屋面积离群点\n",
    "\n",
    "* 因为数据来源于公开平台,所以里面难免存在房租的混乱数据,这些数据有可能就是离群点,所以对这些离群点进行剔除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3f8f30ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 原始的房租箱图\n",
    "new_data[\"price\"].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2b1cadf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 剔除后\n",
    "new_data[new_data[\"price\"] <= 12000][\"price\"].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bffb5dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = new_data[new_data[\"price\"] <= 12000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "59d63165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data[\"area\"].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cfb2c714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data[new_data[\"area\"] <= 150][\"area\"].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9eca1066",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = new_data[new_data[\"area\"] <= 150]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c6c93d3",
   "metadata": {},
   "source": [
    "### 三、数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2750e1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\",{\"font.sans-serif\":['KaiTi', 'Arial']})   #这是方便seaborn绘图得时候得字体设置\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.dpi\"] = 144 # 提高绘图的像素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "65ba8015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>home_type</th>\n",
       "      <th>home_dire</th>\n",
       "      <th>admi</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "      <th>floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>整租</td>\n",
       "      <td>2室2厅1卫</td>\n",
       "      <td>西南</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>73.00</td>\n",
       "      <td>7800</td>\n",
       "      <td>低楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>整租</td>\n",
       "      <td>1室0厅1卫</td>\n",
       "      <td>东南</td>\n",
       "      <td>南山区</td>\n",
       "      <td>43.00</td>\n",
       "      <td>6000</td>\n",
       "      <td>低楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>整租</td>\n",
       "      <td>2室2厅1卫</td>\n",
       "      <td>东南</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>66.51</td>\n",
       "      <td>4000</td>\n",
       "      <td>低楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>整租</td>\n",
       "      <td>3室2厅2卫</td>\n",
       "      <td>东北</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>89.00</td>\n",
       "      <td>9500</td>\n",
       "      <td>高楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>整租</td>\n",
       "      <td>1室1厅1卫</td>\n",
       "      <td>南</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>47.00</td>\n",
       "      <td>2800</td>\n",
       "      <td>中楼层</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  type home_type home_dire admi   area  price floor\n",
       "0   整租    2室2厅1卫        西南  宝安区  73.00   7800   低楼层\n",
       "1   整租    1室0厅1卫        东南  南山区  43.00   6000   低楼层\n",
       "2   整租    2室2厅1卫        东南  龙岗区  66.51   4000   低楼层\n",
       "3   整租    3室2厅2卫        东北  宝安区  89.00   9500   高楼层\n",
       "4   整租    1室1厅1卫         南  龙岗区  47.00   2800   中楼层"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b00268c2",
   "metadata": {},
   "source": [
    "#### 3.1 面积与房租的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "594cf231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='area', ylabel='price'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_data = new_data[[\"area\", \"price\"]]\n",
    "type_data.plot.scatter(x = \"area\", y = \"price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "659ea069",
   "metadata": {},
   "source": [
    "可以看出合租的房屋个人面积也比较小"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81b00e2d",
   "metadata": {},
   "source": [
    "#### 3.2 房型和房租的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c71acc16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "home_type\n",
      "1室0厅0卫      3277.611940\n",
      "1室0厅1卫      4668.560016\n",
      "1室1厅0卫      3500.000000\n",
      "1室1厅1卫      4866.201450\n",
      "1室1厅2卫      2866.666667\n",
      "1室2厅1卫      5495.021277\n",
      "1室2厅2卫      4010.661654\n",
      "2室0厅0卫      3000.000000\n",
      "2室1厅1卫      5367.068706\n",
      "2室1厅2卫      7339.523810\n",
      "2室2厅1卫      6087.071872\n",
      "2室2厅2卫      6611.588235\n",
      "3室1厅1卫      6563.956619\n",
      "3室1厅2卫      4912.899263\n",
      "3室2厅1卫      6906.095162\n",
      "3室2厅2卫      6789.027280\n",
      "4室1厅1卫      6742.857143\n",
      "4室1厅2卫      7700.000000\n",
      "4室1厅3卫     10500.000000\n",
      "4室2厅1卫      8900.613497\n",
      "4室2厅2卫      7259.646739\n",
      "4室2厅3卫      6371.428571\n",
      "4室3厅2卫     10000.000000\n",
      "5室1厅2卫      5250.000000\n",
      "5室2厅2卫      7953.367876\n",
      "5室2厅3卫      8900.000000\n",
      "6室2厅2卫      8500.000000\n",
      "未知室1厅0卫     3200.000000\n",
      "Name: price, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='home_type'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1584 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_data = new_data.groupby(by=\"home_type\")[[\"price\"]].mean()[\"price\"]\n",
    "print(type_data)\n",
    "type_data.plot.line(figsize=(15,11))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b30f2afc",
   "metadata": {},
   "source": [
    "上图可以看出并非房型越大，就会需要更多的房租,也存在着递增的趋势"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0544b203",
   "metadata": {},
   "source": [
    "#### 3.3 房租和房屋朝向的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "45ee873b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "home_dire\n",
      "西     6544.778325\n",
      "西北    6474.551440\n",
      "东北    6226.908140\n",
      "南     5978.059112\n",
      "东南    5582.233333\n",
      "西南    5523.067114\n",
      "北     5211.844015\n",
      "东     5077.739224\n",
      "Name: price, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='home_dire'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1584 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_data = new_data.groupby(by=\"home_dire\")[[\"price\"]].mean()[\"price\"].sort_values(ascending = False)\n",
    "print(type_data)\n",
    "type_data.plot.bar(figsize=(15,11))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa74c411",
   "metadata": {},
   "source": [
    "可以看出房的朝向对房租依旧存在影响，但是不明显"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b91d65",
   "metadata": {},
   "source": [
    "#### 3.4 房租和行政区关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "15612000",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "admi\n",
      "南山区    7386.499759\n",
      "福田区    7348.113774\n",
      "龙华区    5622.558148\n",
      "宝安区    5417.676447\n",
      "罗湖区    5048.159436\n",
      "盐田区    4314.516129\n",
      "光明区    4201.202186\n",
      "龙岗区    4069.705961\n",
      "坪山区    3126.689189\n",
      "Name: price, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='admi'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5mTFjRgYOHJiOHTtm0KBB2WqrrVJTU5M99tgjLVu29IgaGqXa2trceeed2XjjjbP55punc+fOKSsrS2lpaVq0aFF/nGX99dfPCy+8UOS0fByFGw1KU09T1qtXr+y00071zz1Jku233z5/+MMf6g9KnXnmmVmwYEE6d+5crJiw2srLy/Pggw9m1113rf/9XFVVlYEDByZJBg8enHvvvTdJMmXKlIwcOdKZszRatbW1GTFiRAYOHJgrr7wyS5cuTZLMmDEjTzzxRA499NBsueWWOfPMM7Nw4cKcffbZWbRo0ceeTATroj/96U/517/+ld133z39+vWr/11/yimnpH379vnrX/+ao48+OqNGjcpRRx2VrbfeOnvuuWcefPDBnHfeeTnzzDOL/RXgU3FSHM3BmWeemV69emWXXXZJu3btsmLFivpb7/Xo0SMDBgzIaaedlpEjR+bHP/5xpk2blhNOOKHIqeGTlZeXZ8KECfX7l1dccUWmTp2agQMHpkuXLnn77bfTs2fPPPTQQ9ltt91ywgkn5PTTT09paWn22muvIqeHT6+kpCS33HJLNtpoo49dr7a2NnvuuWcDpeKzULjRYDT1NHUVFRUfOtu7a9euqzzEumvXrnnnnXcUbjQqjz/+eL73ve9l4MCB+eUvf1l/tcNWW22Vt956KxUVFWnfvn1KS0tTVVWVN998U+FGo/Xqq69myZIlOeOMM5IkrVq1SpLcdddduffee3PttdemQ4cOmTt3bvr165fOnTvnW9/6Vo499tj87ne/c9IQjcbYsWMzefLkjB07Nu3bt89mm22WsrKyXHHFFRk5cmTefffddOrUKa+//nqOO+64bLLJJjnrrLPyxS9+Mf3798/s2bPTvXv3Yn8N+EROiqOpe/jhh3PnnXemdevWue6667JixYrU1NRk+fLl9cdZ/ud//ie/+tWvsuGGG+baa6/NOeeckwcffDBf/OIXi5wePt4Hz9/8QFVVVSZPnpx99903nTp1qi/c/v3vf+fHP/5xysrKctVVV+Xiiy/O4MGD06NHjyIlh9VTUlLyiWVbkrRo0SIdO3bMrFmzUlVV1QDJWF0KNxqMpp7moLy8fJXXe++99yp/ALt375758+c3dCwoyI477pj7778/99xzTw466KDsvvvumTRpUioqKjJv3rzMnz8/nTt3zv/+7//WP7h60KBBxY4Nn8l6662Xa6+9NhtssEGSpKzs/c3l73znO/nOd76TKVOmZOzYsTnllFMycuTItG/fPkceeWSWLFmSa665JieddFIR08On97//+7/p3r17ampqMn369EydOjUPPfRQHnvssTz33HNZtGhRttpqq1x66aVZsWJFjj/++PrPDhgwoIjJYfU4KY6mrm3btjn88MPrrzyeM2dOunXrlpqamowdOzYbbrhhDjjggPr1S0tLc9ZZZ+W6664rVmT4TJYvX5527drl+eefT/L+dvvcuXOTJCeccEL9dntJSUl+8IMf5OWXX1a40SRtsskm+etf/5r999+/2FH4CAo3GoymnubgP3fmq6urc9hhh2X69OlZsmRJlixZkpkzZ2bq1Knp1atXevbsWcSk8Om1adMmG2+8cU455ZR85StfyQ9/+MMccsghGTFixIfWraqqytSpU4uQEtaMLl26pEuXLvWvW7dunZqamvoTKjbeeOPss88+mTt3bkpKSvKVr3wlSXLsscd+6ueowLrgg6vTysvLs/HGG2fjjTdOz549c9lll+Xqq69ObW1tvv/97yd5v3j+4GpPaIycFEdTtuOOO+Yvf/lL/evTTz8911xzTcrLy1NZWZlevXp95BX4/3lbVVjXvfvuuxk+fHgWLlyYmpqazJ8/P61atcrdd9+dV199NRUVFWndunVatWqVioqKtGvXrr54/r9/A6CxGzBgQMaPH69wW0cp3FgnaepprBYsWJBddtklixYtyvLly5O8f5Cqc+fO6dixY9q2bZsNN9ww7777bpGTwmfTr1+/3Hrrrbngggtyzz335Etf+tIq73ft2jXPPvtsccLBWtC+ffvMnDkzffv2rV/Wo0ePTJs2Lcccc0z9srKysuy2225us0ejttlmm2WzzTZL8v5JRLW1tfn2t7+dZcuWZcaMGfnxj3+cM88804ErGh0nxdGUtWzZMitWrMiSJUuSvH8LvkWLFqV9+/bp0aNH/RVA0Ji1atUqDz/8cJYuXZorrrgic+fOzbx581JXV5eVK1dm5cqVmTdvXhYtWpS33347U6ZMyeTJk9OiRYscfvjhOfXUU4v9FaAgv/jFLzJp0qS0atUqCxYsyHPPPZfXXnstffr0KXY0/g+FG+skTT2NUW1tbWbNmpX99tsvQ4YMSZ8+fdKzZ88P3XMcGruysrKceeaZueOOOz50NXL79u1TW1tbxHSw5rz00ku56667Mnbs2BxwwAE58cQTU1lZma5du2bOnDkfWn+vvfbK3LlzFW40Kr/97W/z2GOPZezYsVlvvfVy8skn17+30UYbZcCAAZ6BQqPnpDiaug9u/96uXbvU1tZm3333zbJly7Jy5cq0bds277zzzionCkFjU1pamtLS0rRq1So/+tGPkrx/7HDnnXf+r59ZsmRJ5syZs8qJc9AYVVdX56abbsqBBx6YIUOGZMqUKXn++edz00031d9OmHVHSV1dXV2xQ0Dy0U39Aw88oKmnUVm5cmVKS0uLHQOK5qWXXsqcOXOy6667FjsKrDFPPvlkrrnmmuy///45+OCDs3Dhwpxwwgm58cYbP7RuXV1dSkpKipASVl91dXW22267HHzwwR/aWa+urs69996b6urq7Lrrrundu3eRUkJhamtrs/fee2fYsGFOiqNJqqury9SpUz/yER6LFy/O7Nmz07t3b1cnAzRSNTU1mT17dv1zxj9Y5vf6uknhxjqhuro6u+yyyypN/a9//esceuihmnoAYJ1SW1ubYcOGZcKECcWOAgVZunRpnnnmmQwbNqx+2eWXX57bbrst9913X9Zbb738/e9/z9ixYzN79uwMGjQoffv2zde+9rV07ty5iMlh9TgpDgCAhqBwY52gqQcAGpOddtopf/rTn4odA9a4z3/+8xk9enSOOOKIVZbfeOONOf/887Pvvvvm/PPPt50OAADwfyjcANai6urqnHHGGRk6dGi23HLL9OzZc5WHtkNT9Oabb2bSpEl5/PHH8+Uvfznbb799sSPBGnfbbbfl0EMPLXYMWOOmTJmSjTfe+CPfW7p0adq2bdvAiQBYE/79739n0qRJmTp1aiZMmJArr7wyG264YbFjwRpnf5Sm5uGHH87666+fIUOGFDsKn0JZsQMANGX33XdfHnjggTzwwANJkhYtWqRLly7p379/9tlnn3z5y19OWZlfxTQds2bNyj/+8Y88++yzefDBBzNp0qTcf//9xY4Fa5yyjabqv5VtSZRtNAuTJ0/O+uuvn8rKymJHgTVq4MCBadWqVebNm5c33ngjN910U84444xix4I1yv4oTUF1dXUWLlyYbt26JUl+97vfZeTIkUVOxaflKC9AgaZPn15/ZuD9999f/3yT5P0d9qOOOipf/vKXs2LFiixYsCAzZszI9ddfn0svvTStWrXK/vvvX8z4sEZVVVVl+PDhGT58eHr37p2LL744s2bNSlVVVbGjAQB8rOrq6owaNSq//vWvM2DAgGLHgTWub9++GT16dObMmZO///3vxY4Da5z9URqr5cuXp7S0NC1atMg555yT2bNn56qrrkqSdOzYMYsWLSpyQj4t9zWjqF5//fU89NBDxY4BBbniiivy5JNPZuXKlTn77LNz8MEH57TTTsvChQuz1VZbZdttt80mm2yShx56KDvttFO+8IUvpH379nn66aeVbTRpw4cPT11dXSZPnlzsKPCZvPLKK1m8ePGnWnfSpEm2aWh06urq8uKLLxY7BjSImpqa3HzzzXn77bf/6zrTpk3LvHnz0qlTpwZMBoWbP3/+Kq/PPffc1NTU/Nf1d91117z11ltrOxYUlf1RGpOrrroqN910U5KkTZs2mTRpUm6//fYkyaabbpp58+YVMx6rQeFG0dTW1uakk07K9773vWy33XY5+OCDc/TRR2f06NE59thjc8IJJ+TUU0/NFVdckZdffrnYceG/2mCDDTJ+/PiUlpamW7duGTduXEpLSzNz5sxsueWW9TsyTz75ZObNm5cVK1aksrLSs9xoVJYvX55f/OIXee2111ZZ/sgjj+Tmm2/+yM9069YtHTp0yJtvvtkACWHNWrBgQY488sj6WwJ/kp/97GeZNm3aWk4Fa9YVV1yRkSNH5ne/+12xo8Bad+WVV+bSSy9NeXn5Ksv/+c9/1v/7g+1zhRuNyV//+tfssccemT17dv2yO++8M++8885//UyXLl2ybNmyhogHa4T9UZq6srKy/P73v0+SnHbaafn973+fp556KtOmTcvAgQPNcSPiaC9FM2HChEybNi2jR4/OGWeckaOOOirDhw/PTjvtlE033TQdO3bMtGnTMm7cuHzpS1/K5ZdfXuzI8JF23XXXvPDCC0mSfffdN5tsskm+8IUvZNasWenZs2fGjx+fvffeO7NmzcoxxxyT008//WPPNoR10YMPPphf//rXadeuXaZOnZpHH300SXLBBRfk3HPPzXXXXfeRn6uqqsqCBQsaMCmsGdddd13efvvtj71qbfny5TnttNNy5plnZunSpZ7JSaNz9dVXp1u3bvnxj3+cE0880a1qaNL+9Kc/5fjjj0/79u3rl/373//OYYcdlhEjRuSRRx5Jq1at0rZt25SWlhYxKayeRx99NCtWrMj5559fv6xz584f+zu9tLQ0rVq1aoh4sEbYH6WpGzZsWF588cXMnz8/zzzzTJYuXZoxY8bkjTfeSLdu3TJ9+vQsXrw4ixcvztKlS1NdXZ26urpix+YjOCpA0bRp0yb77bdfTj755I9d7+CDD05dXV2GDh3aMMFgNQ0ePLj+lmM77bRTJk2alEGDBtVfFdGuXbuMHj06L7zwQrp06ZIddtght912WzEjw2p7/fXXc/TRR6dz586566678vzzz2evvfbKo48+mvHjx+eiiy7K1KlTM2bMmFWu3mzdunURU8NnN23atOy55555/fXX8/e//z1bb731h9a57bbb8uSTT+baa6/N1Vdfndra2iIkhc9uo402yvXXX5/a2tqMGzcuF1xwQc4777wkyY033pjq6uocc8wxRU4Ja0bHjh3z9a9/fZVl48ePz+67757WrVvn+uuvz7Rp09yFgkbnzDPPzNFHH53vfve7+ctf/pLtt98+m2666YduM/mfXn755frnjkNjYH+Upm6jjTbKhhtumGOOOSaTJk1KkpSXl6esrCwtWrTIsmXLMnTo0NTW1qa2tjZ1dXUpLS1NZWVlevfuna985Ss58MADi/wtSBRuFMFll12WE088Mdtss039GSn/aeHChXnrrbcycODA1NTUpF+/fjnnnHPSsmXLIqSFT1ZSUpLddtstSTJgwICce+65eeuttzJhwoQceuih6datW77yla+kZ8+eSZLtttsuTz75ZDEjw2o7/PDD62+vtPnmm+e3v/1tkqRly5bZZ599svPOO+fUU0/NSSedlEsvvbT+zPC5c+dmgw02KFpu+KwGDx6c/v37Z9ddd83UqVNzzTXX5Gtf+1r9rciWLl2ali1b5s4770zPnj1TVVWVFStWFDk1rJ7Pfe5zWbx4cdZff/2MHDkyTz31VJLk2WefzXnnnZeSkpK89957Oe6441zxQ6Ny5ZVX5oEHHsiAAQPSoUOHlJWVpaysLBdccEGSpKKiIoMHD87AgQNz/PHH13+urq4ue+21V7Fiw2fWs2fPXH/99bnmmmuy/fbbp1+/fpk8eXJ+//vfZ+DAgenTp0+6d++eVq1aZcqUKfnFL36Rk046qdix4VOzP0pzsOOOO6Zly5YZN25cunXrtsodVL761a/m17/+dSoqKoqYkE/DqVs0uDvvvDOvvPJKysvLU1JSUr/85ptvzsKFC/PYY4/lvvvuS/L+bQ7OP/98ZRvrvO9+97tJkurq6txzzz158cUXc+aZZ6ZVq1Zp37597rnnnixZsiSzZ8/OzTffnFdffTVXXnllli9fXuTk8On857NMtt5668yZMyfjx4/P5Zdfnr/97W9ZunRpzjnnnHTr1i3HHXdcZsyYkXHjxqWsrCy77757EZPDZzN48OBMnz49LVq0SP/+/TN+/Pi8+OKL9e8fe+yx+cIXvlB/MkXXrl09C4VGp1+/fhkzZkzuvffedO/evf65s3379k1JSUlGjx6dBx98MAcffHCmTp1a5LTw6T3//PPZfPPNs+mmm2bTTTfNwIEDM3To0PTu3Ttt27bNggULUlJSkn79+uXyyy/PJZdcUn+LeAeyaEyOPfbYnHXWWbnqqqty7733pmfPnnn44YezYMGCXHfddVm0aFGmTZuWRx99NFdffXUuu+yyPPfccxk7dmy++MUvFjs+fGr2R2kOhgwZkl133TU9e/ZMWVlZrrvuupx++ulJ3t9u/+DKN9ZtrnCjQbz77rupqalJx44ds8UWW+Qf//hH+vfvn7q6upx99tk56KCDMmnSpLRv3z79+/fPI488kiTOpKXRqKysTPL+2VUPPvhg+vTpU//eu+++m/POOy+DBg1Kr1690qtXr3z5y1/OZpttpkym0ZgwYUIWLVqUYcOGpbKyMptsskluuOGG9OrVK/fee2/9LQ1WrlyZ2traHHbYYTnqqKNy1113pU2bNsWOD6ulpqYm3bp1y4MPPli/rH///pk8eXK22WabJO//vv/3v/+d7bffPsn7z0r5vw9xh3Vdr169UltbmwkTJuTWW29N9+7dk7w/zz169MjXv/71HH/88bn44otzyCGHZNy4cdlxxx2LnBo+2ZVXXvlf33vttdfqt9WXL1+eP/7xj+ndu3d++ctfZvny5Zk7d26WL19uO5113sSJE/PHP/4xffr0ycyZM9OmTZuUl5entLQ0ixcvzuc///n84Ac/SNu2bVc52RkaI/ujNAcf3OY6Sf3VyGPHjk2SbLjhhnnxxRc/8lEHrFsUbjSIiy66KA8++GD222+/lJSU5KWXXkry/k7+DTfckMcffzydO3dOy5Yts99++2XWrFlFTgyfzQfFW21tbf19w/fYY4987Wtfq78KAhqbZcuW5Ywzzsg777yTsrKybLbZZlm4cGG22267fOtb30qHDh0+9Jkf/ehH2Xzzzev/PwGNxeLFizN8+PB07949Xbt2rV8+aNCgTJw4Mbfccks6dOiQioqK/OEPf0h5eXlatmyZRYsW1T/PExqLXr16Ze+9987BBx+clStX5rTTTsurr76aysrKVFVVZfLkydlxxx1z+umnZ6ONNsq3v/3t3HDDDRkyZEixo8NqufLKK3PssccmSe67776ceOKJSd4/eeIHP/hB/ckUSTJq1KgsWLBglb8BsC7q2bNnJkyYkKqqqg+9t2TJkhx66KHZb7/9svnmm+e8885L+/bti5ASCmd/lObiP+e1pqYmN910UwYNGpQk6dGjR2bPnl2saKwGhRsNory8PF/96lczb968zJw5M+PHj88LL7yQTp065eKLL84OO+yQl19+OV//+tfz6KOPZsmSJdlzzz1TXl6edu3aZfDgwTn99NOdlcU6acqUKRk3blzOO++8VFZW5pRTTknHjh1z7rnnpkWLFjn//POzxx575Ljjjit2VPhMampqcumll2b99dfPe++9l5kzZ+YPf/hDHnjggdx7773ZcMMNs//+++eAAw5I27Ztk7y/g3PhhRdmhx12KHJ6WD2PPvpo2rZtm0suuSQXXXRR/fIBAwbkJz/5Sfr27ZsuXbpkwYIFefHFF+tv67Fs2bKP3NmHdVn37t3zzjvvJEkWLFiQxYsX55BDDknv3r1TXl6epUuXJkn+8pe/ZOrUqTnllFNy3HHH5YEHHjDvNCq33357Ro0alZYtW6aioiKTJk3K5ptvniSZOXNmJk+enKlTp6akpCRz5szJ/PnzFW6s8/7zFntJMm/evFx//fXZc889M2TIkLz33nsZMWJEHnvssXzxi1/M+eefn1122aVIaeGzsz9Kc3T22WfnmmuuSZK8+OKL6dixoztNNBIKNxrEB/ebTZJ33nknI0aMyGWXXZannnoqU6ZMyZZbbpk2bdqkb9++Oeigg/Lvf/877du3z+6775527dqlR48eyjbWWZdcckkWLFhQfybK9OnT889//jMHH3xwvvOd72TOnDlZf/31c//996ekpCRlZWVp3bp1unfvnn79+qW8vLzI3wA+Xvv27VfZUdlkk03Su3fvvPTSS7n11lvz9NNP53e/+13GjRuXr371qxk9enTKy8tzzDHHFDE1fDbbbLNN9t1337Ru3TpVVVWZN29eOnfunP79+6ddu3Z5+OGHk7z/fKArr7yy/rZls2fPzvHHH1/M6LDaKisrM2HChEyYMCEVFRVZtGhRbrvtttx11135zW9+k5qamqxYsSI//OEP84UvfCEjR47M1KlTM3bs2Pz0pz8tdnz4r1auXJm99947rVu3To8ePbJ48eKceeaZqaqqyltvvZWZM2dmzJgxSZI33ngjvXv3zrBhw9KvX78k7/9OHzBgQDG/Aqy2s88+O9OmTcvIkSOTJL17984+++yT0aNH57777sv3v//9HHvssTnyyCOLGxRWk/1RmoPp06enVatW9VctV1VV5dZbb83o0aMzePDgfPe7313lhFDWXQo3Gtx6662Xqqqq9OrVKyNGjMg555yTkSNHZvvtt892222X3r17p0+fPnn44Yez2WabFTsufKKampr8/Oc/r3/9/e9/P6+99lrWX3/9/OhHP0qrVq2yYMGCrFixIkuWLMkbb7yRN954I++9916OOOKIHHLIIcULD59Rv3798uqrr6asrCxDhw7N0KFD89Zbb+Wyyy7Lfvvtl5///OfZdNNNix0TVtuGG25Y/+8+ffpkypQp6dy5c9q1a5d27drVv9e7d++8/fbb9a+7dOlSf6UQNCZbb711TjnllCTJTTfdlLZt2+aHP/xh9tlnn5x88sl58skn8/3vfz/Dhw9Pkpx66qk54IAD8sorr6R///7FjA7/1axZs9KnT59sueWWqayszLvvvpuFCxdms802y3rrrZdf/epXOf3009OqVat06dIl+++/f5Jkzpw5qamp8YgDGqWRI0dm2223rX+0Qc+ePfOvf/0rG2ywQfbff/987nOfy3HHHZfq6up885vfLHJaKIz9UZqS5cuXZ+TIkZk/f35atGiRioqKrFixIhMmTMiNN96Y8vLyLFq0KGPHjs1pp51W7Lh8AoUbRfHBGVfJ+79Ufvazn6VPnz559tln069fv7Rp0yZXXHFFERPCp3fttdeu8nro0KH55je/mauvvjo777xzzjvvvPzxj3/MZZdd5v7hNBklJSXZfvvtV1nWo0ePnH/++Xn22Wdz8skn5+yzz852221XpIRQuP97n/yddtqp/t+dOnVa5cSg0tLS1NTUNGg+WBM+mNuFCxdmxYoVeeKJJzJixIgMGTIkt99+e0444YR06dKlfv2ysrKceuqpufHGG3P22WcXKzZ8rPXXX7/+NkxJ0rFjx/pHGCTJhAkTcv/99+eggw7KjBkzss8+++Tdd9/NwIEDs2LFilVOsIDG4v9um3ft2jVLliypf73BBhvkt7/9bU466aQ89NBD2XfffRs6Iqwx9kdpSl577bXstttu2X333dOvX7906dIlJSUl2X333TNhwoSUl5enpqYmBxxwQD7/+c+7PfA6rkWxA9A87bXXXvX/btGiRXr16pXk/ds4derUKRUVFTn66KOLFQ8K0qJFi7z33nuZPXt2Kisrc9555+XLX/5yRo8enUWLFhU7Hqwx/+3EiG222Sa/+c1vMm7cuFWuAILGZNGiRZk4cWLeeuutLFy4MEny4x//eJV1/m/ZUF5eXr8uNBZTp07N//z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      "text/plain": [
       "<Figure size 2160x1584 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_data = new_data.groupby(by=\"admi\")[[\"price\"]].mean()[\"price\"].sort_values(ascending = False)\n",
    "print(type_data)\n",
    "type_data.plot.bar(figsize=(15,11))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9484e0f2",
   "metadata": {},
   "source": [
    "可以看出福田区的房租均值最高，最低的是坪山区 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "797e97e3",
   "metadata": {},
   "source": [
    "#### 3.5 房租和楼层的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "623fe78c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "floor\n",
      "低楼层    6051.186854\n",
      "高楼层    5938.260834\n",
      "中楼层    5597.351635\n",
      "Name: price, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='floor'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1584 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_data = new_data.groupby(by=\"floor\")[[\"price\"]].mean()[\"price\"].sort_values(ascending = False)\n",
    "print(type_data)\n",
    "type_data.plot.bar(figsize=(15,11))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fedacf5",
   "metadata": {},
   "source": [
    "由此可知楼层对房租的影响并不大"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09035d32",
   "metadata": {},
   "source": [
    "#### 3.6 结论\n",
    "\n",
    "* 由上图的数据可视化显示，type、home_type、home_dire、admi、area、floor这些特征对房租皆有影响，但是还不清楚哪个特征才是影响房租的根本原因，所以继续对本数据进行挖掘"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a302453",
   "metadata": {},
   "source": [
    "### 四、特征工程\n",
    "\n",
    "* home_type、home_dire、admi、area、floor这些特征对房租皆有影响，但是其全部为文本格式，不利于进行机器学习的挖掘；\n",
    "* 解决方案：因为通过上面的数据分析和可视化,我们可以感觉到这是一个回归类的问题,所以我们根据同一特征对房租的影响进行数字排序，这个序列即为本回归的影响系数;\n",
    "* 因为type全为整租类型,所以不在探索的范围"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e1e2578",
   "metadata": {},
   "source": [
    "#### 4.1 数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d9742230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>home_type</th>\n",
       "      <th>home_dire</th>\n",
       "      <th>admi</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "      <th>floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>整租</td>\n",
       "      <td>2室2厅1卫</td>\n",
       "      <td>西南</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>73.00</td>\n",
       "      <td>7800</td>\n",
       "      <td>低楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>整租</td>\n",
       "      <td>1室0厅1卫</td>\n",
       "      <td>东南</td>\n",
       "      <td>南山区</td>\n",
       "      <td>43.00</td>\n",
       "      <td>6000</td>\n",
       "      <td>低楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>整租</td>\n",
       "      <td>2室2厅1卫</td>\n",
       "      <td>东南</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>66.51</td>\n",
       "      <td>4000</td>\n",
       "      <td>低楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>整租</td>\n",
       "      <td>3室2厅2卫</td>\n",
       "      <td>东北</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>89.00</td>\n",
       "      <td>9500</td>\n",
       "      <td>高楼层</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>整租</td>\n",
       "      <td>1室1厅1卫</td>\n",
       "      <td>南</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>47.00</td>\n",
       "      <td>2800</td>\n",
       "      <td>中楼层</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  type home_type home_dire admi   area  price floor\n",
       "0   整租    2室2厅1卫        西南  宝安区  73.00   7800   低楼层\n",
       "1   整租    1室0厅1卫        东南  南山区  43.00   6000   低楼层\n",
       "2   整租    2室2厅1卫        东南  龙岗区  66.51   4000   低楼层\n",
       "3   整租    3室2厅2卫        东北  宝安区  89.00   9500   高楼层\n",
       "4   整租    1室1厅1卫         南  龙岗区  47.00   2800   中楼层"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "188eff30",
   "metadata": {},
   "source": [
    "##### 4.1.1  房型排序替换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cd0cacc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-28-99097fc9f1f2>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_data[\"home_type\"][new_data[\"home_type\"] == i] = home_type_dic[i]\n"
     ]
    }
   ],
   "source": [
    "# 排序\n",
    "a = new_data.groupby(by=\"home_type\")[[\"price\"]].mean()[\"price\"].sort_values()\n",
    "# 将顺序计入字典\n",
    "home_type_dic = {}\n",
    "flag = 1\n",
    "for i in a.index:\n",
    "    home_type_dic[i] = flag\n",
    "    flag += 1\n",
    "# 进行替换    \n",
    "for i in home_type_dic:\n",
    "    new_data[\"home_type\"][new_data[\"home_type\"] == i] = home_type_dic[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3407b389",
   "metadata": {},
   "source": [
    "##### 4.1.2 房的朝向排序替换 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2dfde732",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-bd8ffdc86360>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_data[\"home_dire\"][new_data[\"home_dire\"] == i] = home_dire_dic[i]\n"
     ]
    }
   ],
   "source": [
    "# 排序\n",
    "a = new_data.groupby(by=\"home_dire\")[[\"price\"]].mean()[\"price\"].sort_values()\n",
    "# 将顺序计入字典\n",
    "home_dire_dic = {}\n",
    "flag = 1\n",
    "for i in a.index:\n",
    "    home_dire_dic[i] = flag\n",
    "    flag += 1\n",
    "# 进行替换    \n",
    "for i in home_dire_dic:\n",
    "    new_data[\"home_dire\"][new_data[\"home_dire\"] == i] = home_dire_dic[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "020c32a6",
   "metadata": {},
   "source": [
    "##### 4.1.3 房所处的行政区排序替换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "61907efd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-30-7f5fd68a0adb>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_data[\"admi\"][new_data[\"admi\"] == i] = admi_dic[i]\n"
     ]
    }
   ],
   "source": [
    "# 排序\n",
    "a = new_data.groupby(by=\"admi\")[[\"price\"]].mean()[\"price\"].sort_values()\n",
    "# 将顺序计入字典\n",
    "admi_dic = {}\n",
    "flag = 1\n",
    "for i in a.index:\n",
    "    admi_dic[i] = flag\n",
    "    flag += 1\n",
    "# 进行替换    \n",
    "for i in admi_dic:\n",
    "    new_data[\"admi\"][new_data[\"admi\"] == i] = admi_dic[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc19270",
   "metadata": {},
   "source": [
    "##### 4.1.4  楼层排序替换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cf560a2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-07cb5ef7bf62>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_data[\"floor\"][new_data[\"floor\"] == i] = floor_dic[i]\n"
     ]
    }
   ],
   "source": [
    "# 排序\n",
    "a = new_data.groupby(by=\"floor\")[[\"price\"]].mean()[\"price\"].sort_values()\n",
    "# 将顺序计入字典\n",
    "floor_dic = {}\n",
    "flag = 1\n",
    "for i in a.index:\n",
    "    floor_dic[i] = flag\n",
    "    flag += 1\n",
    "# 进行替换    \n",
    "for i in floor_dic:\n",
    "    new_data[\"floor\"][new_data[\"floor\"] == i] = floor_dic[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8336b78d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>home_type</th>\n",
       "      <th>home_dire</th>\n",
       "      <th>admi</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "      <th>floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>整租</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>73.00</td>\n",
       "      <td>7800</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>整租</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>43.00</td>\n",
       "      <td>6000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>整租</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>66.51</td>\n",
       "      <td>4000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>整租</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>89.00</td>\n",
       "      <td>9500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>整租</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>47.00</td>\n",
       "      <td>2800</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36409</th>\n",
       "      <td>整租</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>58.30</td>\n",
       "      <td>4200</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36410</th>\n",
       "      <td>整租</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>61.35</td>\n",
       "      <td>5100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36412</th>\n",
       "      <td>整租</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>76.46</td>\n",
       "      <td>4000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36413</th>\n",
       "      <td>整租</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>76.00</td>\n",
       "      <td>5300</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36414</th>\n",
       "      <td>整租</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>40.00</td>\n",
       "      <td>5500</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25171 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      type home_type home_dire admi   area  price floor\n",
       "0       整租        13         3    6  73.00   7800     3\n",
       "1       整租         7         4    9  43.00   6000     3\n",
       "2       整租        13         4    2  66.51   4000     3\n",
       "3       整租        18         6    6  89.00   9500     2\n",
       "4       整租         8         5    2  47.00   2800     1\n",
       "...    ...       ...       ...  ...    ...    ...   ...\n",
       "36409   整租        18         2    3  58.30   4200     3\n",
       "36410   整租        19         4    5  61.35   5100     1\n",
       "36412   整租        15         5    5  76.46   4000     2\n",
       "36413   整租        13         5    8  76.00   5300     2\n",
       "36414   整租         8         5    8  40.00   5500     3\n",
       "\n",
       "[25171 rows x 7 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1040ce4",
   "metadata": {},
   "source": [
    "#### 4.2 特征选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d85af535",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 剔除租房类型\n",
    "new_data1 = new_data.iloc[:, 1:]\n",
    "# 对所有特征的数据进行格式化\n",
    "new_data1[:] = new_data1[:].astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "543b703e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>home_type</th>\n",
       "      <th>home_dire</th>\n",
       "      <th>admi</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "      <th>floor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>66.51</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>89.00</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   home_type  home_dire  admi   area   price  floor\n",
       "0       13.0        3.0   6.0  73.00  7800.0    3.0\n",
       "1        7.0        4.0   9.0  43.00  6000.0    3.0\n",
       "2       13.0        4.0   2.0  66.51  4000.0    3.0\n",
       "3       18.0        6.0   6.0  89.00  9500.0    2.0\n",
       "4        8.0        5.0   2.0  47.00  2800.0    1.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c1b9f923",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "home_type    0.416085\n",
      "home_dire    0.156812\n",
      "admi         0.579817\n",
      "area         0.545283\n",
      "price        1.000000\n",
      "floor        0.085050\n",
      "Name: price, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = new_data1.corr()\n",
    "corr = corr['price']\n",
    "print(corr)\n",
    "corr[abs(corr)>0.4].sort_values().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce74403",
   "metadata": {},
   "source": [
    "通过上图可以看到，home_type、area、admi这3个特征对price的影响最大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d2bc11f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price', ylabel='area'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_data = new_data1[[\"area\", \"price\"]]\n",
    "type_data.plot.scatter(x = \"price\", y = \"area\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d757ed32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price', ylabel='admi'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_data = new_data1[[\"price\", \"admi\"]]\n",
    "type_data.plot.scatter(x = \"price\", y = \"admi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c83f6a92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price', ylabel='home_type'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_data = new_data1[[\"home_type\", \"price\"]]\n",
    "type_data.plot.scatter(x = \"price\", y = \"home_type\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dca87eba",
   "metadata": {},
   "source": [
    "### 五、建立模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "013ee16c",
   "metadata": {},
   "source": [
    "#### 5.1 KNN算法\n",
    "\n",
    "构建此模型只需要保存训练集即可。要对一个新的数据点做出预测，算法会在训练集中寻找与这个新数据点距离最近的数据点，然后将找到的数据点的标签赋值给这个新数据点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "88ab7c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "from  sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(new_data1[[\"home_type\",  \"admi\", \"area\"]], new_data1['price'], random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "62249b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)\n",
    "knn_score = knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b360f3",
   "metadata": {},
   "source": [
    "#### 5.2 决策树\n",
    "\n",
    "决策树是广泛用于分类和回归任务的模型。本质上，它从一层层的if/else问题中进行学习，并得出结论。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c55fc949",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "detree = DecisionTreeClassifier().fit(X_train,y_train)\n",
    "# print('测试集精度{:.2f},训练集精度{:.2f}'.format(detree.score(X_train,y_train),detree.score(X_test,y_test)))\n",
    "detree_score = detree.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea8c010f",
   "metadata": {},
   "source": [
    "#### 5.3 支持向量机\n",
    "\n",
    "SVM学习每个训练数据点对于表示两个类别之间的决策边界的重要性。通常只有一部分训练数据点对于定义决策边界来说很重要：位于类别之间边界上的那些点。这些点叫作支持向量（support vector），支持向量机正式由此得名。\n",
    "对新样本进行预测，需要测量与它每个支持向量之间的距离。分类决策是基于它与支持向量之间的距离以及在训练过程中学到的支持向量的重要性（保存在svc的dual_coef_属性中）来做出的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7978a184",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm = SVC(kernel='rbf',C=10,gamma=0.1).fit(X_train,y_train)\n",
    "svm_score = svm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c5e3493",
   "metadata": {},
   "source": [
    "#### 5.4 线性回归\n",
    "\n",
    "是回归问题最简单也是经典的线性方法，。线性回归寻找参数w和b，使得对训练集的预测值与真实值的回归目标值y之间的均方误差最小。均方误差是\n",
    "（mean squared error）是预测值和真实值之间的平方和除以样本数。线性回归不需要要手动给定参数（即没有调参），这是一个优点，但也因此无法\n",
    "控制模型的复杂度。并且普通最小二乘估计系数依赖于模型的独立。当条件相关,设计矩阵的列有近似线性关系,设计矩阵接近奇异和结果,随机误差的最小二乘估计变得高度敏感的观察到的反应,产生很大差异。这种情况下可能出现多重共线性的,例如,当数据收集没有实验设计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3b9ac637",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression().fit(X_train,y_train)\n",
    "lr_score = lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "56a17d74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>算法</th>\n",
       "      <th>算法精确度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>knn_score</td>\n",
       "      <td>0.904020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>detree_score</td>\n",
       "      <td>0.911648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>svm_score</td>\n",
       "      <td>0.879072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lr_score</td>\n",
       "      <td>0.623114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             算法     算法精确度\n",
       "0     knn_score  0.904020\n",
       "1  detree_score  0.911648\n",
       "2     svm_score  0.879072\n",
       "3      lr_score  0.623114"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {}\n",
    "dic[\"knn_score\"] = knn_score\n",
    "dic[\"detree_score\"] = detree_score\n",
    "dic[\"svm_score\"] = svm_score\n",
    "dic[\"lr_score\"] = lr_score\n",
    "\n",
    "pd.DataFrame(list(dic.items()), columns=[\"算法\", \"算法精确度\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "651f63f0",
   "metadata": {},
   "source": [
    "这里可以看出线性回归模型的精确度较低，所以我们接下来的调优，就使用KNN算法、决策树算法、支持向量机算法"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6bff1f",
   "metadata": {},
   "source": [
    "### 六、模型调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "22e6fee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入网格收索工具\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e006cae",
   "metadata": {},
   "source": [
    "#### 6.1 KNN算法调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3c261d41",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:666: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=6.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型最高分为：0.904\n",
      "最佳参数设置：{'n_neighbors': 1}\n"
     ]
    }
   ],
   "source": [
    "# 将遍历的参数定义为字典\n",
    "params = {\"n_neighbors\":[i for i in range(1,11)]}\n",
    "\n",
    "grid_search = GridSearchCV(knn, params, cv=6)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"模型最高分为：{:.3f}\".format(grid_search.score(X_test, y_test)))\n",
    "print(\"最佳参数设置：{}\".format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1af81ee",
   "metadata": {},
   "source": [
    "#### 6.2 决策树调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f03eb830",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:666: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=6.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型最高分为：0.912\n",
      "最佳参数设置：{'criterion': 'gini', 'max_depth': 66, 'max_features': 1}\n"
     ]
    }
   ],
   "source": [
    "# 将遍历的参数定义为字典\n",
    "\n",
    "# 调整树的深度和考虑最大特征数\n",
    "params = {\"max_depth\":[i for i in range(1,101)],\n",
    "         \"max_features\":[i for i in range(1,4)],\n",
    "         \"criterion\":[\"gini\", \"entropy\"]}\n",
    "\n",
    "grid_search = GridSearchCV(detree, params, cv=6)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"模型最高分为：{:.3f}\".format(grid_search.score(X_test, y_test)))\n",
    "print(\"最佳参数设置：{}\".format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c17ce7c",
   "metadata": {},
   "source": [
    "#### 6.3 支持向量机调优 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cbf2e218",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:666: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=6.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型最高分为：0.892\n",
      "最佳参数设置：{'C': 10}\n"
     ]
    }
   ],
   "source": [
    "# 支持向量机模型计算量大，不适合普通pc端进行调优，这里只用于简单的演示  \n",
    "# C参数是正则化参数，与线性模型中用到的类似，它限制每个点的重要性（或者更确切地说，每个点的dual_coef）。\n",
    "params = {\"C\":[i for i in range(1,11)]}\n",
    "\n",
    "grid_search = GridSearchCV(svm, params, cv=6)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"模型最高分为：{:.3f}\".format(grid_search.score(X_test, y_test)))\n",
    "print(\"最佳参数设置：{}\".format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "20ba88ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:666: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=6.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型最高分为：0.909\n",
      "最佳参数设置：{'gamma': 0.9}\n"
     ]
    }
   ],
   "source": [
    "params = {\"gamma\":[i/10 for i in range(1,11)]}\n",
    "\n",
    "grid_search = GridSearchCV(svm, params, cv=6)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "print(\"模型最高分为：{:.3f}\".format(grid_search.score(X_test, y_test)))\n",
    "print(\"最佳参数设置：{}\".format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "259b37ea",
   "metadata": {},
   "source": [
    "经过调优，可以知道决策树的精确度最高，达到91.2%，所以链家租房房租的预测，选用决策树模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e25762",
   "metadata": {},
   "source": [
    "### 七、模型使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3e44c9e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精度0.95,训练集精度0.91\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# 输入调好的参数\n",
    "detree = DecisionTreeClassifier(criterion='gini', max_depth=68, max_features=1).fit(X_train,y_train)\n",
    "print('测试集精度{:.2f},训练集精度{:.2f}'.format(detree.score(X_train,y_train),detree.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0d5e066",
   "metadata": {},
   "source": [
    "因为我们最后要完成的是构建一个模型，为用户服务，对输入的特征进行预测，所以这步进行改进"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0201fe90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['1室1厅2卫', '2室0厅0卫', '未知室1厅0卫', '1室0厅0卫', '1室1厅0卫', '1室2厅2卫', '1室0厅1卫', '1室1厅1卫', '3室1厅2卫', '5室1厅2卫', '2室1厅1卫', '1室2厅1卫', '2室2厅1卫', '4室2厅3卫', '3室1厅1卫', '2室2厅2卫', '4室1厅1卫', '3室2厅2卫', '3室2厅1卫', '4室2厅2卫', '2室1厅2卫', '4室1厅2卫', '5室2厅2卫', '6室2厅2卫', '5室2厅3卫', '4室2厅1卫', '4室3厅2卫', '4室1厅3卫'])\n",
      "请输入房型：1室1厅1卫\n",
      "dict_keys(['坪山区', '龙岗区', '光明区', '盐田区', '罗湖区', '宝安区', '龙华区', '福田区', '南山区'])\n",
      "请输入房所属的区：宝安区\n",
      "请输入房面积：50\n",
      "预测的价格大约为：RMB [3300.]\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    print(home_type_dic.keys())\n",
    "    home_type = input(\"请输入房型：\")\n",
    "    home_type = home_type_dic[home_type]\n",
    "    print(admi_dic.keys())\n",
    "    admi = input(\"请输入房所属的区：\")\n",
    "    admi = admi_dic[admi]\n",
    "    area = eval(input(\"请输入房面积：\"))\n",
    "    \n",
    "except:\n",
    "    print(\"!!!\" * 20)\n",
    "    print(\"   \"*5, \"请正确输入数据\")\n",
    "    \n",
    "dic = {\"home_type\":home_type,\"admi\":admi,\"area\":area}   \n",
    "ar = pd.DataFrame(dic, index=[0])\n",
    "pre = detree.predict(ar)\n",
    "print(\"预测的价格大约为：RMB\", pre)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a536a58",
   "metadata": {},
   "source": [
    "### 八、分析结论"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05db2972",
   "metadata": {},
   "source": [
    "* knn算法：适合小型数据集，是很好的基准模型，适合大部分类型预测\n",
    "* 决策树算法：速度快，不需要缩放\n",
    "* 支持向量机：对参数敏感，计算量大"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3683e2",
   "metadata": {},
   "source": [
    "因为影响房租的特征有3种，所以这里并不适合使用线性回归，knn和支持向量机并没有决策树算法的精确度高，所以我选择具备ifelse特点的决策树模型进行预测\n",
    "\n",
    "* 房型越大、行政区越繁华、房屋面积越大，房租的也会越高"
   ]
  }
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